Web access to knitted HTML output

We have decided to take a leap and try plot_ly and knit to html output for this milestone. The html page is hosted as Github Pages and is accessible in this address: https://tin6150.github.io/phw251_group_z/milestone4_groupZ.html

Project and milestone overview

{Pasted to group’s gdoc}

R setup and data preparation

(This code block omitted for brevity, please refer to source at our github repo )

Visualizations & Code

Rural Counties Deserving More HCAI Funding

Table 1 shows that the 5 counties of Siskiyou, Inyo, Mariposa, Plumas and Modoc have low population density (they qualify as Rural per National Rural Development Partnership’s definition), have a high median age, and fairly high ratio of renters. More importantly, they have one of the highest percentage of chronic diseases, and no HCAI fundings that are “In Closure” as of 2022-08-11.

Note that the latest population data is from 2012.

viz_focus = viz_fund_dem_chron %>%
  filter( rural_class      == "rural",
          high_med_age     == TRUE 
          ##high_rental    == TRUE  
          )

focus_table = viz_focus %>%
  select( County, 
          pop12_sqmi,
          #rural_class,  
          #low_pop,
          med_age,
          #high_med_age,
          rent_own_ratio,
          #high_rental,
          pct,                    # prevalence,         
          Numeric_Cost, 
          #fund_per_cap,
          #`Number of OSHPD Projects`,
  ) %>% 
  arrange( desc( pct )) %>%
  rename(
    `Pop Density` = pop12_sqmi,
    `Median Age` = med_age,
    `Rent:Own Ratio` = rent_own_ratio,
    `% Chronic`      = pct,
    `HCAI Fund in 2022` = Numeric_Cost
  ) %>%  
  head( 5 ) 

kable( focus_table,
       booktabs=T,
       digits=c(0,1,1,2,2,0),
       format.args=list(big.mark=','),
       caption = "Rural Counties with high median age, rental ratio, and chronic disease rate",
       )
Rural Counties with high median age, rental ratio, and chronic disease rate
County Pop Density Median Age Rent:Own Ratio % Chronic HCAI Fund in 2022
Siskiyou 7.1 46.8 0.54 2.86 0
Inyo 1.8 45.5 0.57 1.88 0
Mariposa 12.6 49.2 0.47 1.72 0
Plumas 7.7 49.5 0.44 1.69 0
Modoc 2.3 46.0 0.46 1.43 0

Mortality Rate by County

Below is a graph of Mortality Rate for Chronic diseases (as defined by CDC) across 11 rural counties (as defined by National Rural Development Partnership) The 5 counties of focus have the highest mortality rates in this group.

Note that we don’t have disease data for Alpine or Sierra county.

chronic_focus_counties = inner_join(
    demographics_chronic,
    rural_counties,
    by = "County"                   ) %>%
  mutate( ctyColor = case_when(
    County == "Siskiyou" ~ "red", 
    County == "Inyo"     ~ "darkorange" , 
    County == "Mariposa" ~ "blue", 
    County == "Plumas"   ~ "darkblue",
    County == "Modoc"    ~ "darkcyan",
    TRUE                 ~ "rgb(187, 216, 228)"
  ) )


fig3 = plot_ly( data = chronic_focus_counties ) %>%
    add_trace(
            x = ~County,
            y = ~pct,
            name = 'Chronic Mortality Rates by Counties',
            marker = list(color = ~ctyColor),
            hoverinfo = "text",
            text = ~paste(round(pct, 1), "%" ),  
            type = 'bar') %>%
   layout(
            title = "Chronic Mortality Rates For California's Rural Counties", 
            yaxis = list( title="% Mortality Rate"),
            xaxis = list( title="County", categoryorder='total descending' )
         )

fig3

Table showing most common disease by rural county

This table shows the most common chronic disease in each of the rural counties, while also showing the number of people who have the illness in the year 2020. The counties of Alpine and Sierra did not have chronic disease data available. *HTD= Heart Disease, CAN= Cancer

table_data_e<- inner_join(rural_not_rural, chronic_mortality_data, by= "County") %>% 
select(c("County", "rural_class", "Cause","Count", "Year")) %>% filter(Year%in%2020) %>% 
filter(rural_class=="rural") %>% 
group_by(County,Cause) %>% summarize(count_cause=sum(Count)) %>% arrange(County,desc(c(count_cause))) %>% slice(c(1,11,21,31,41,51,61,71,81,91,101))

table_data_e[1,2]<-"Not Available"
table_data_e[9,2]<-"Not Available"

common_chronic<- kable(table_data_e, col.names=c("County", "Chronic Disease", "Number Reported"),
digits=0, booktabs=T, escape=F, align="ccc", caption="Most Common Chronic Disease by Rural County in 2020")
common_chronic
Most Common Chronic Disease by Rural County in 2020
County Chronic Disease Number Reported
Alpine Not Available 0
Colusa HTD 172
Inyo HTD 226
Lassen HTD 352
Mariposa HTD 200
Modoc HTD 68
Mono CAN 22
Plumas HTD 226
Sierra Not Available 0
Siskiyou CAN 756
Trinity CAN 62